Machine Learning part1--Linear regression with one variable
Definition
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
假设用 P 来评估计算机程序在某任务类 T 上的性能，若一个程序通过利用经验 E 在 T 中任务上获得了性能改善，则我们就说关于 T 和 P，该程序对 E 进行了学习。
Example: playing checkers.
●E = the experience of playing many games of checkers.
●T = the task of playing checkers.
●P = the probability that the program will win the next game.
In general, any machine learning problem can be assigned to one of two broad classifications:
Supervised learning and Unsupervised learning.
1.1 Supervised Learning
In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.
在监督学习中，我们得到了一个数据集，并且已经知道我们的正确输出应该是什么样子，认为输入和输出之间存在着一种关系。
Supervised learning problems are categorized into "regression" and "classification" problems.
In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function.